{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "939d2012",
   "metadata": {},
   "source": [
    "# 项目实战：电信用户流失数据可视化\n",
    "\n",
    "**工具包**：`numpy` `pandas` `matplotlib`\n",
    "\n",
    "**数据**：WA_Fn-UseC_-Telco-Customer-Churn.csv\n",
    "\n",
    "**数据集说明**\n",
    "\n",
    "字段名|字段描述\n",
    "-|-\n",
    "customerID|用户ID\n",
    "gender|性别（Female & Male）\n",
    "SeniorCitizen|老年人（1表示是，0表示不是）\n",
    "Partner|是否有配偶（Yes or No）\n",
    "Dependents|是否经济独立（Yes or No）\n",
    "tenure|客户职位（0-72，共73个职位）\n",
    "PjoneService|是否开通电话服务业务（Yes or No）\n",
    "MultipleLines|是否开通了多线业务（Yes、No or phoneservice）\n",
    "InternetService|是否开通互联网服务（No、DSL数字网络，fiber optic光纤网络）\n",
    "OnlineSecurity|是否开通了网络安全服务（Yes、No、No internetservice）\n",
    "OnlineBackup|是否开通了在线备份业务\n",
    "DeviceProtection|是否开通了设备保护业务\n",
    "TechSupport|是否开通了技术支持服务\n",
    "StreamingTV|是否开通了网络电视服务\n",
    "StreamingMovies|是否开通了网络电影服务\n",
    "Contact|签订合同的方式（按月、一年、两年）\n",
    "PaperlessBilling|是否开通了电子账单（Yes or No）\n",
    "PaymentMethod|付款方式（bank transfer，credit card，electronic check，mailed check）\n",
    "MonthlyCharges|月费用\n",
    "TotalCharges|总费用\n",
    "Churn|该用户是否流失（Yes or No）"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "519761cb",
   "metadata": {},
   "source": [
    "### 1. 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "820d8f74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "data = pd.read_csv(\"WA_Fn-UseC_-Telco-Customer-Churn.csv\")\n",
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "df81e8d9",
   "metadata": {},
   "source": [
    "## 导入 seaborn 设置主题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4ce20478",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(font='SimHei')\n",
    "sns.set_theme()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8aba077a",
   "metadata": {},
   "source": [
    "### 2. 观察数据\n",
    "\n",
    "**(1) 数据形状**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e25bbdb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "66b29bed",
   "metadata": {},
   "source": [
    "**(2) 查看数据所有字段名**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "af7ad7a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
       "       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
       "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n",
       "       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',\n",
       "       'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges',\n",
       "       'TotalCharges', 'Churn'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns.values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2bd0baf0",
   "metadata": {},
   "source": [
    "**(3) 查看每个字段的数据类型**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "38e6aec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID           object\n",
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges         object\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "147755bf",
   "metadata": {},
   "source": [
    "**(4) 输出数据详细信息**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f4f8780a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b1b140e5",
   "metadata": {},
   "source": [
    "### 2. 数据预处理\n",
    "\n",
    "**(1) 对数据字段进行重命名，使其满足大驼峰命名规范**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b090f028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CustomerID', 'Gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
       "       'Tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
       "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n",
       "       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',\n",
       "       'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges',\n",
       "       'TotalCharges', 'Churn'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.rename(columns={'customerID' : 'CustomerID' , 'gender': 'Gender', 'tenure':'Tenure'})\n",
    "data.columns.values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ae6596f7",
   "metadata": {},
   "source": [
    "**(2) 数据转换**\n",
    "\n",
    "- 将老人数据转换成Yes No\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f1327597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     5901\n",
       "Yes    1142\n",
       "Name: SeniorCitizen, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data['SeniorCitizen'] = data['SeniorCitizen'].map({1:'Yes',0:'No'})\n",
    "data['SeniorCitizen'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ded27fe5",
   "metadata": {},
   "source": [
    "### 3. 数据分析"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a053d56d",
   "metadata": {},
   "source": [
    "**(1) 解决绘图的中文乱码问题**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dabe0df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimHei']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "84029e02",
   "metadata": {},
   "source": [
    "**(2) 依据Churn字段的数据绘制用户流失的柱状图，x轴为用户是否流失，y轴为用户数量**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cbe6628e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '用户数量')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 构造x轴和y轴数据\n",
    "data_churn = data['Churn'].value_counts()\n",
    "\n",
    "x = data_churn.index\n",
    "y = data_churn.values\n",
    "\n",
    "# 绘图\n",
    "plt.bar(x, y,color=['#ffaf65','#f36886'])\n",
    "plt.xticks(x,['留存','流失'])\n",
    "plt.xlabel(\"用户是否流失\")\n",
    "plt.ylabel(\"用户数量\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "87db3f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '用户数量')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "axes_count = sns.countplot(data=data,x='Churn')\n",
    "axes_count.set_xticklabels(['留存','流失'])\n",
    "axes_count.set_xlabel(\"用户是否流失\")\n",
    "axes_count.set_ylabel(\"用户数量\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "836dd544",
   "metadata": {},
   "source": [
    "**(3) 依据Churn字段的数据绘制用户流失比例的饼图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8b2b97f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '用户流失比例')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rKkHZK/VBwIQQzLbHsQ9dCgNbk44kWRXFRNffQ3TLA4SnHs3kl53MoDV0eW2tkOFTCcqwFAJLS+AJBrZg77qkcld2kWpQjoj+fhfRrQ+QP+tUpp18JD2xozfWpnvZM5Wg7FbOGpoDT94NYe/7FWy8L+lIIjs3VKR02d8oX7+Y+jeeScOc6XR5GFQZym6oBGWnrIHmwFAbgHn0CszKG8HrwUSqn9/aSfG7vyZYdCAtb/4nGutq6NTZpLILKkF5lvrQ0hyayqHWj10J0WDSkUT2WvzYKga/+H+EpzyHyWe/gAHr6XJO5xXJM6gEZYfQGFpDCIe2YW+9UBvdZfxznujGe4nufoT8Oacz7ciFdHjHUKz9hVKhEhQAJoSWhtBgHvk95okb0PmekioDQ5QuupJg4UNMfPsrKOZzdDqHqlBs0gEkWXlrmVawNHSvwF77+cqGdxWgpFT8+GoGv/B/hHc8xNRcSF2gh8Cs00gww5pDQ33gsPddDBvuSTqOyNgoR5R+cy32ziVMeNfZ1DfU0eE9se5UkUl6GpRBeWuZljfUdy3H/u3zKkDJJLdmE0NfvgDz97uYGgbUB0HSkSQBGglmzITQ0BB47P0Xw/rFSccRSZZzlP98K9G9j9H0wddRU19Lh1aQZopGghkRGMOUvKGhZzX22s+pAEWexm9qZ+grFxDc8yjTwpC81UNjVuhvOgNqAsvUgiW37C/Y274Dxd6kI4lUnyim/Mu/Uvrx5bQZaFIRZoL+llNuQmhotRH29nMxy/6KVn6K7F68ZDmDX/oRdRu3MdlaAmOSjiSjSCWYUtbA5JyhoX899u9fhPblSUcSGTd8Tz/Fb12Mv+YOpoZB5YbRkkpaGJNChcAyKWdg+bXYx65Aoz+RfeAhuvp23GOraPng68iHAd26k33q6OlNyjSGlkmBw975A+xjl6MCFNk/bvVGhr56IXVbOmizFk2OpotKMCUs0JYzNA1uxl73Jdj6aNKRRFLD9w4w9D8XYxY/wtQwINR1wtTQdGgK5KyhLWexq2/GPPw73fJIZDQ4R/lXV+NWbWTK68+g3XmGdK/CcU8lOM49ef3P3PdzzPq7k44jknrx7Q8ytGErEz90Dn2BpUd3pBjXNB06jtUFlkmhx97+XRWgyBhyqzcy9B8XUr+1i0lVdp2wXC7v8X26OjvGIMn4oBIcp5pCQ4spYW/6L2hflnQckczxPf0MfeNnhI+voi2wiTyYXvn737Fl06Yd/9/Z0cGnPvh+3E5Wsa5dtYr+vj4A3n3O68YsY7XTdOg4NDE01Ja7sbd8C4rdSccRya7YUbzgD+RfezqTTzqCrbEb07tR5HM5PvzOd3D2Oedw6w3Xk8vnWb9mDR951zso1NRQKhb5zFe/ztTp07ngu+fy1ve8l/kHH0xzS8uYZax2Zk1vv9bQjxMGmJQz5HvXYu/4HkTFpCPJfvJnfpeB/7wIv7Uz6Siyn8IXHU/uFaewNY4pu7F7WO3p7qa+vh5jLR9/77v54Cc+ScvEidQ3NlJbWwvA5o0bee8bzuHAeQcBsGLp48xbsBCAcqnI+z76MY485tgxy1xNNBIcJ548ASbY/CD23ou0AlSkykR/vwvf3cvkt7yUbcZRHOUFMw/ddx+/ufjnfPSznyMIQy792UUcf/LzmL9oEbdefz1//uMf+Mq3vk0Qhlz4vXP57Nf/k+NPfh4A73n9OZz7k5+Oar7xQtcEx4HQGKbmA4InrsPe82MVoEiVihc/SvEHv2GSMaN+1NohRxzB7Dlz+PZXv8L9ixfz4/O+z2033sAn3vde/vzHP9Df18cPvv1NyuUycw6av6MAhwYHCUONf56k6dAql7eWtrzBPPRrzOpbk44jI0zToelkZ7RR85E30R1Y+kZ5RNjb00NtbS1LHniAWQccwPLHH2PRoYexfu0auru6aGmdxLlf/xr5QgGAOIpY9cQK5i1YANvXtQ4NDfKt8y+gobFxVLNWIz0dqGJPFqC9+3zYvCTpOCIyTG79Vgb/6yKaPvnPkAtGrQidc3z03e/iW+f/iJv/fi0nPf8FXPi97/H/vvQlrrnySj7y758F4IeX/HLHx/z4+99jaGiQE553Cm9657tGJdd4ounQKqUCFBnffHs3Q//9M5pKEQ2jdG/Cm//+dxYeeigTWlro7+tj8tSpWGuwNiAIgme9/+Lbb+e+u+7iexf9nLtvu5Xf/fIS/BiuZq1GKsEqlLOmUoCLL1ABioxjvqOboW/8jKZyRMMIXyMsDg1x0Q9/wJve+U66u7pYsXQpMw84YEepee9Z+ugj3HbjDQwODPCz8/+Pn/7wPL78rW9TW1fH1793Ho8teYj3vP4cLr/sMvp6s3mzbZVglclZw+S8rSyA2fRg0nFEZD/5jp7tI8LyiBbh+rVrOfX002loaOQzH/og7/jgB3HOUSqVaG1r485bbuZrn/k0fT09vOt1ryGOIr5zwY9pbWsDoLa2ls9+/b/40Cc/xZL77yPI6GIZLYypIk8WoLn3IsyGe5KOI2NAC2Oyw7Q0UvPJf6a3kKN3hK8Rbly/nmkzZuzy7eVymVwuN6J/ZlpoJFglQrO9AO/7uQpQJIV8Zy9D//0zGofKNI7wNcLdFSCgAtwNlWAVCI1hSsFi7v+FDsIWSTHf1cvQf19EY7lM/SjvI5Th0d9CwkJjmFywmAd+iVl3Z9JxRGSU+e4+hr75C5qdH/UN9bJn+htIULC9AO1Dl2LW3p50HBEZI35bF0PfuYSJGGpUhInSdz8hhspZoPbRyzGrb0k6joiMMbd+K0Pfv5RWa8mP0j5C2TN95xPSljPYDXdjVlybdBQRSYh7Yj3Fn1zOpMASmmq6NW92qAQT0BIacj1rsA9cknQUEUlY/OByyr+7rnJjXvXgmFMJjrGG0FIX9WHvPA/86B6sKyLjQ3TzfcQ33EObtagHx5ZKcAzVBAETbIy99VsQDSUdR0SqSPnyG+HRVUzU9cExpe/2GMlZQ2sO7G3nwmBH0nFEpAqVLvoT+a5eGrVidMzoOz0GAmNoy1WOQ6NrVdJxRKRalSOK3/01Tc5Ts5O7QMjIUwmOMgO05cAs+4uOQxORPfLdfQyddxmt1mjF6BhQCY6y1pwh2Pwgdumfk44iIuOEW7mB4q+voS3QQpnRphIcRU2hoTCwGXvvRUlHEZFxJr7jIeI7HmKSFsqMKn13R0nBWhqtx95xHvg46TgiMg6Vf3MtwYatTFARjhp9Z0eBpTINahdfAMXupOOIyHjlPMUf/pb6ONYZo6NE39VR0Joz2NU3w5aHk44iIuPdwBDF//sdE60l0EKZEacSHGENoSU/1IF5+HdJRxGRlHBPrCe65g5ada7aiFMJjqCcNUwI2H4kmq4DisjIKf/lNoJN7TTp+uCI0ndzhBhgUs5g7r8YBrYlHUdE0sZ7iuf/nkYqC+9kZOg7OUIm5gx2432Y9XcnHUVEUsp391H8yRW0BlYP3iNE38cRUBdYaqJ+3RpJREZd/PAK4tse1EHbI0Tfxf0UGkNLzmDv+D7EpaTjiEgGlH73d3I9/dTrfNH9phLcT5NyYB7+LfRuSDqKiGRF7Cj9+I80BxarbRP7RSW4HxpCS9C3EbPyxqSjiEjGuHVbKF93NxNVgvtFJbiPAmOYEBrsPT9JOoqIZFT5qlvIDwxRp9Nk9pm+c/toYuhh2TXQvyXpKCKSVVFM8cI/0mwt2ke/b1SC+6A2CMhHA7o9kogkzq3eSHzLA7QYPZzvC33X9pIBJuaoTIPqVBgRqQKly2+gUCxRq9Wie00luJeaQwPr74GO5UlHERGpKEcUf3w5LdboJrx7SSW4F/LWUmcddsllSUcREXkGt2Id8X2P696De0nfrb3QGvrKqTDlgaSjiIg8S/n311FvLaG2TQybSnCYGkOL7V4D6xcnHUVEZKd87wClK2+mRSU4bCrBYQiMoSk02PsuSjqKiMhuRdcvJjdYpEaLZIZFJTgMLaHHLP0LDLQnHUVEZPeco/SLP9OiweCwqAT3IG8tBeMxK65NOoqIyLDEj63CP7GeRp0ks0f6Du1Bc+Awj/xBd4gQkXGl9KuraQoCAl0f3C2V4G4UAkvOFTGrb006iojIXvHbuijfcA8TVIK7pRLcjZbAYx+6TCfDiMi4VP7LbdQaoy0Tu6ES3IXaICAodsOGe5OOIiKyb4aKlK++nQnqwF1SCe5Cc+ixD/4K8ElHERHZZ+XrF1ODIafbTOyUSnAn6kOL7d0AWx9NOoqIyP4plSlddQsT9Hx+p1SC/8AAEwKDffDXSUcRERkR0U33kveevM4VfRZ9R/5BQ2gxHcuga1XSUURERkYUU778Ribo8s6zqASfxgBNoa2sCBURSZHotgfJlWMKGg0+g74bT9MYWtj0APRtSjqKiMjIco7yH65nQtI5qoxKcDsDNIYG+/iVSUcRERkV0d2PEMaxrg0+jb4T29WFAXSuhN6NSUcRyYyfrnqQU2+8hOffdAkXr1my4/W95SIvvfU3rBvs3e3HO+859cZLePEtv+bFt/yaty++CoBfrn2Ys277LWsHegC4ZvPK0fsixhPnKF99B426NrhDmHSAatFkHfaxPyUdQyQzHuzewtVbVvLL48+i5GLevvgqzpg8h6k19fzgift4yZQ5zKxt3O3nWD3QzeET2jjvqBc/4/U3bF3Lhw86lpvb13Gcm0ZLvmY0v5RxJbr1fupecQqBj4m9ylAjQSqnw9ihTmhflnQUkcyosSFfO+RUZtY2Mre+mWk1DWwtDvBYbzvXbV3NOw88Yo+f4/HeDhY2TNzJWzyNYYH+qMzizo0c1zJt5L+A8WqoRHTL/TTqKDVAJQhAk42xj12RdAyRTFnQOJF5DS0AbC0OsLnYz0ENzXxz6V3Uhzk+/8jN/GbdY/jdjFYe7+vg6s0refEtv+acO//Ig91bADAYuspDWAP1YW5Mvp7xpHztXdSHAapBTYeSt5bQl2DDfUlHkbQxAdS3QcNkaJiCa5gGYQ0EIdgQbA5jDIV3nY0fKkEc44tl/LYu6OzBdfXiu/rwXb347j5wLumvaNSct+JeXj19AUv7OnmwZwsfnncsU2rq+d6KezAGXjtj0U4/bmK+hs8tOonnts7g2i2r+Opjt3HZCa/kxVPmcP7K+3nx5APZVhzkTXddwY+OfgkNYX6Mv7Lq5Lt6iZasoOHgOfTG2b5BQOZLsNE6zPK/oTNCZZ/VtkDjdGiYgm+cjm+aAfVtmHw9LioRuZiSCYgI8N7jAe8rP3HGg5/cgtn+nNwYCIwhjB2Bc4TGEIQBNhfiB4vE67fiHl+NW7meePVGGBhK9EsfCbe2r+P+7s386viz+P36pZzSOos3zz4UgME44m9bVu2yBN86+7Advz998oH81+N30FEa5DUzFvKaGQv59dpHuX7ral4yZS73dm3m1EmzxuRrGg+iP99K46Fz6c12B2a7BK2B2jDErL456SgynhgLLXPx047CTz8GCo2UoxJlLGUTEjlHFHuigeL2D7BUKi/a+z/LeyhHUI4IjCE/awr5WVMoxDGFQh4/MES8cgP+8VVEDy3Ht3eP4Bc6+jYN9fMfj97GN484jdogR32YY2pN/Y63F2xA425Gb79fv5RXz1gAQDGO6ImKO55Q3N25kWNapnLd1tVMyBXoLevG2E/n1m/Brd9C3fQ2BqLsNmGmS7AhsPj1izHlgaSjSLXL1cHkQ3EzjoG2Q4idY8BbhhyUhiKeury+D0U3TLH3DMYxg0++olgizAUUFh1AYcEsal51Gr6rl/jOJUT3Pobf1D5qWUZCycV87MG/87YDDuWwpjYAjpowhZ+vWUJnaYjaIOSPG5Zy+uQDd/k5/rZlJTVBwAsmzebHqx/ksKa2HStBl/V18qZZh2CAvqjEjD2sNM2i6OrbaXjby8nyI6BZ09uf2XnAGTUB9sb/hN4NSUeRalTbgp9xHH7GcZim6RTLJQYIGYpd1S4tL1hLLVBnLQwMEd21hHjxo7j1W5KO9iw3bF3Dxx+6jtanbV/41IITGYwjfrzqQbrLRU6ZNJMvHHwyW4uDfPiBa7n0hLOf8TlW9Xfz7w/fyLrBXp7TPIVPLzyRaTUN9JSL3NO1idPaDuBnqx/iio3L+ckxL2VCrjDWX2Z1s4a6b3yYzUBUpT/Toy2zJVgbBkwc2IC96T+TjiLVpnE6buHLYeoRDEYxA95QjN24u2qct5ZaY6g3Bt/RTXT17UT3PgYZnvqSZ8u9+jSKJx9Fd4oXXu1OZktwcugpLPkVrLsr6ShSLVoX4Ba9AloOpCfy9EXjr/h2pSYIaHSOvLWUr7+b8nWLoX9wzx8oqWfaWqj93LtYXyonHSURmbwmaA3kwxxsvD/pKJI4A9OOwh18Nq52Ij2xoX8ofSOloThmCAido+kFx1B3+gmU71xC9Jfb8J09SceTBPmtncQbtlI7pZXBDG6XyORIsCEMmNC+BHv3+UlHkaTYED/rJPyilxHZAj0uyNQDgDWGRmtoCALK1y+m/JdboZjNkYBAeNwh8PoXszWD1wUzWYJTc57c4gtg6yNJR5EkTD4Md/Q/UzI5emJLMaPXQqCyJ7HZGArOU/rdtcR3LtGW2SwKA+r+5yNsirN3nmjmjk0LjSEwBrY9lnQUGWs1zbgTP0R83Htp9wW2lsl0AUJl20W7c2yzhuCcM6j53Luxc2YkHUvGWhRTvvth6jN4i6XMfcV1gcGsuxN8th/8ssXg574Qd/p/0Ne8gI1Fx1Csv/+nKznH5tjR3dJE4cNvIP+eV0Gd7ryQJfFN91KfwcNEM1eCDQGY1bcmHUPGSl0r7tRPU150NpuLju4UrfgcDQNxzMZyRHHRgdR+6b3Yg2YmHUnGiFu3BTNQzNwNdzP11eatrZwO070m6SgyBvzsk3GnfZGeumlsLvnMbgbeWx7oco6OMKDwodeTO+v5lSXVknrRHQ9Rl3SIMZapEqy3XueEZkFYgzvpI0SHncOWsqc30tTnvhiKHZuiCP/8o6n51NsxE5uSjiSjLLr7YeqCTNVCtkqwLgwwa+9IOoaMpppm3As+x8CEOWwqecpOo7/94TxsdY6+tmZqP/9ugqMXJh1JRpHf1I7v6aeQoSnRzHylNYGFvs0wUN2HCst+aJqBO+3z9ASNdJZVfiOpN3ZscZ78215OeOZzk44joyi+cwm1SYcYQ5kpwXrjMKtuSjqGjJZJC3GnfIpOl6M3VgGOhrJzbI5igjOfS+6NZ6LbkqdTfO9jlQPYMyIzX2lNGGI2PZB0DBkFfuaJuBM/xLYIBrT1YVTF3rM5juG4Qyi877UQBklHkhHmNm6DwaHMrBLNxFeZtxZKvTDUlXQUGWFuwctwR76ZzSVHUQU4JjywJXaU58+i5mNvgRrdnihtorsfoTYjI/1MlGCNNZiNGgWmirG457yd+KAXs6nkiLQAZsy1O8fglInUfObtmMasLaxPt/j+pdRmZEtRJkqw1kSYTQ8mHUNGkDv2PZSnPofNJY/LyD/WatTlHP2NdRQ+/ladMJMibvUGgiDMxPbQ1JegAXJhHtqXJh1FRohbdDZR2yFsLXud/lIFemLHYGMdNR95ExRySceRkeA88fI11ATpv+ab+hKsCQJ852pwUdJRZAT4mSfg552uAqwyXc5RbJ1A4UOvh4wsqEi7+P6l1GbgOnvqf1prTYzZcE/SMWQkTJyHP+otbC07dAmw+nQ4RzS9jfw/vzzpKDIC4kdWUsil/77rqS/BmsBitjycdAzZX3VtuBP/lfayToGpZu3OweHzyL3i1KSjyH7yHd0wMEQu5RcGU12CoTEYV4a+TUlHkf2Rq8M972N0xVa3QKpynsoxa+ELjyM4Yn7ScWQ/xQ8tpybl09up/upqAgubliQdQ/aHCXAn/iv9to5+FeC44Dxsc47C21+BaW5MOo7sh/ih5dRGcdIxRlWqS7COCLvp/qRjyH5wz/lnSg3T6Yo0BTqelJyjB0/h/a/RbZjGsXjpavI1+VSfkJfqEsznCrD1saRjyL6adhRu2lFs02HY41Jv7IjaWsi9/JSko8i+KpaJt3WRS/GUaGq/spy1+KFOiAaTjiL7IqzBHfU2OspoK8Q41u63Xx+cPzvpKLKP4mVrKaR4NJ/aEsxbCx0rk44h+8gd+loGCSg6XQccz5yvbJ3Iv/dVUJ+lG/Skh1+2hkKKrwumtgQLRNj2ZUnHkH3RfCDMOoEuTYOmwlDsGLCW/DlnJB1F9kG8cgP5ML37BdNbgsZD16qkY8jeMgHu2HfRWfZoDJge3c4RHDkfe8C0pKPIXvLbujDeE5h0TommsgQNEOQK0LM+6Siyl/xBZ1DONeq+gCnjgW7nyb/lpboZ7zgUr96Y2vsLpvKrylmL72/XeaHjTV0rfuHLaI/0KJlG/XGMm9hEcPxhSUeRveQeX0UhpUvUUlmC+cBC5xNJx5C95I5+Bz2RJ9atkVKrEyi87nQo5JOOInvBPbGeQpzOf5epLMGCL2tRzHgz7Shc0yx6I02DplnJOQaN0d7BcSZevYlcTTqfuKSyBPPWQNfqpGPIXnCHvIrOOJU/jvIPurwjd+pzMBMako4iw1Us4QYGCVO4OCZ1jzoGCMI89GxIOooMV9vBuEIzQ3F69yLJU5yH/igmfNHxSUeRveA2daTy5JjUfUU5a/F9W8HrAXW8cAe/km6Xuh9F2Y1e78md8hxdGxxH3JqNGgmOB1oUM860zME3TmMgxSdSyLPF3lN0MeHJRyYdRYbJr9tCPkrfivvUlWDOR9iuNUnHkGFyC15KT5y+Z5eyZz0Ycmc+V3eZGCfcxm3kNBKsfjkcDGxLOoYMR6EJJh9Mv0aBmVRyjigMCI9amHQUGQa3uZ0whdPXqSvBwFqV4DjhDzyVwXKc0i24Mhy9xhBqu8T4UCzjBodSd10wfSUYFmCgPekYsifG4ue+kF5tC8y0wTjGtDRhp7clHUWGwW3qIEzZCtFUfTWBMfi4CHEp6SiyJ1MOJ8JQdhoHZl2/dwQn6Ci18cCv2UQuZddwU1eCDHYlHUOGwc04jn5ySceQKjDgPeFzD086hgyDb+8iTNnh9qkqwdAa6NuSdAwZjimHMqQFMQKUnceHAXbO9KSjyB64jh4ClWD1Cg2Y3o1Jx5A9aZqBN5ZIB2XLdgMYgqMXJR1D9sB39lQGGymSqhLM+QgzoJFgtfOTD2VQg0B5mkHvCY85OOkYsge+s4cgl667zKeqBEM89Gt7RLXz049h0KfqR0/2U8k5qKvBTJmYdBTZDd83gAmCVN0XOVWPREEQao9gtQvymAkzKeqwbPkHQ1FMMH920jFkdzy4/sHKIsSUSFUJ2iAHg51Jx5DdmbSAclTSBnl5lmIQYBcekHQM2QPX3UuQouuCqSnBwBh8eRB8ulYupY2feiQDpOuagoyMkosJ5s1MOobsgW/v0UiwGlkDRMWkY8ge+KlHMJSyJdYyMsrOYxrroK4m6SiyG35rp0qwGhkMRENJx5DdqZkAuXqdEiO7VB4qERyo/YJVrW8Am6LtTakpQWuoTIdK9aqfQhTpSDvZtWIQYOfNSDqG7IYfKmJTNJuTohI0mPJA0jFkd+pbiVK1uFpGWhGwCw9MOobszmBRI8FqZDBQ7k86huyGr2ujbLQoRnat7Bx2svYKVjM/UExPcZCiErQGTEklWM1843Ti9DyBlFEQe4+tr4EULbxIGz84lJ7iIEUlaPCYUl/SMWR3GibrvFDZI1eOME31SceQXRkqYlJ0T8HUfCWBd1odWu1qW4i0MlT2II4qN9qV6uQHiliVYPWxONDq0OplQ0xYQ6yRoOxB7D2mpTHpGLILfrCICYOkY4yYFJWgh0glWLVqJ+K0PUKGIbIWq5Fg9SqWMEFqqiNNJQiUNR1ateonETkdmi17FgcWM6k56RiyOyma0UlNCRpjNBKsZjXNxNojKMMQew8TGpKOIbuTng5MTwkCqXp2kj76u5G9oC0SVc6n5iltukrQpOvLSRXnMCpCGa4U3aonlVL0Tzk9reE9pGjZburoFleyNzQSrG4pmnVLTWt4AJOeZbup43VFUIbHg0aCVS49FZiiEgQ0HVrNNBKU4fLbF7pJ9dJIsPpoJFjltD1C9oae0FY3r4Ux1UnXBKuXd6n5RyNjIT0jDaluqWkNjQSrnKZDZZisMfhe3RGmmpkgSM3TlNSUIKAplGqmhTEyTIEB36U7wlQtYyCwKsFq4z0qwWoWR1rsIMMSOo+dOIHgkLnYmZMxjXXoGVQVKeTwcXqu8afrNt9W06FVa7CdIMhBOUo6iVS5wMYEi6YRLJwBYQ4T5iAMYKiI6x3A9/TjO3tw27rx3b347r7K67b/SpSeB+hqZPI5fJyeyxupKUGP0Uiwmg11Y6zFoCUPsnsBHnvf+dDxxFOvtHmYMJ2gYRo0TIFprfg5k/DhHAhqweYxuRzkQihH+P5BfE8/rrMX396F7+zF9/Thu/txPX347j4Y0l1N9kkhj0/RfUHTU4LGQJBPOobshh/sJrR1lFP0D0hGXhCEMNDxzFe6EnSuqrxsZ9jZLKmFhjZM43RonErQPAmmt+Bz0/G2FoJCZWSZywFUyrJvAN/Vi+vowbd3/8PIsg/fN6Bnbk9jCjm800iw6sQmwBcademgmg1sJWyYQxlNV8muGRvCUPc+frSDvs2Vl41P+5zspDALTZimGdA4DeonwbxW/MGz8UEDBIVnTsUOFnF9A/jufnxn9/ap2L5nFSYpmibcpXwOn6LN8ukpQQy+tkUlWMVMz3rCprmoA2VXQmPwpb6xOWy92ANbe2DroztetdOyDGugcRpB43RoaIPprfi5bfhwLthaCJ42FVuK8P0D+N7KVKzb1g1dleuWbvt0rO/ug+L4nYo1hXyqBsbpKUHvoaYl6RiyG6ZrNYUZJ9KrpyqyCzlroXdT0jGeKRqCzpWVl+12PRU7BdM0HRomE7RUpmJdbjoEdZXrljumYj2+f6hSll19+I7uylTs00aVrrsP+gerbyq2Jk+axrupKUHnPdRMSDqG7E73WnI6GFl2o8Y4zOaHko6xjxz0bay8PM1Ol+vVNFemYhumENS3wUGt+EMPxAf1YLdPxeZyEAT4wWLlumV3H76jB9feje/qfeZ1y57+MZuKNQ11RMak5vzQ1JRg7D3kG5OOIbvTu4kgzGNKpap7civVocZ6zLbHk44x+oa6Ki9bHt7xql1NxVYW+UyDhskwoxU/b3JlKjbYPhUbPjkVW37GqljX3gWdlbJ0O65f9kGxvF/RTWMdcRBAlI7tTqkpQec8pqYu6RiyOz7GD3SQs02UUrS6TEaGZfvK0O61SUepHtEQdD5Redlul1OxjVMqhdkwhWDiJJjZggtnVKZigzwmyEE+B87jB56ciu2tTMN29Dw1sux+2lTsTpiWJlyKJnTSU4IAQa5yfqjXyouq1bmSfNtRlNSB8g8KQYDvXIPRObP7wEHvxsrL0+x2KrZxKkFdGyyYiM9vn4oNCk8t9AlsZSq2d6ByKMH2qdhg7ozKzFtKpKYEAXxcxhQa9mN5tYw2u+Ee6toOo0+LY+QfFKzHbBmv1wPHkb2Zim2aAQ1TK1Oxs1rxB02BpkZcrBKsSi6OsIUmlWA12/II+VwOG0WpWmEm+68Gh9mageuB40U0BB0rKi/bGcC95FtEPj3VkapzxmLvoaDFMVUtLuG3Lacm1Dmv8hQDhGEOulYnHUV2y2DydamaDk1XCWKg0JR0DNkDu+4O6knHyjIZGYUgwHev1fX8aldowLt0/R2lrAQDleB4sOlBCrm8rgrKDjXGj+P9gRlS00Ic798Wi2qTqhKMjMU1TE06huxJeQDfvY6aQFOiUlEfGsz6e5KOIXtS25KqqVBIWwk6B00zko4hw2DW3kGdSde0iuyb2iCoLO3vq7Lj0uTZ6lqJSNeT11SVYNl5qG9LOoYMg9l0PzVhelaYyb5rtBF2+d+SjiHD4JoPoGxUglUr9h6Tq9V9BceDwU4Y7KAQpOpHUPZSaEzl0OyN9yUdRYZjwmzKKbtdVOoegeKoqNHgOGFW3kCTTdc/KNk7DQGYVbeA02rh8cA0tFFO2ZGHqSvByDlomJJ0DBkGs+pm8gbdWSLD6sMAs/L6pGPIcNS24L1P3SEXqSvBksnhG6cnHUOGw5Uxy/5KU5Cu1WYyPHVhAF1rYGBb0lFkOBqnU47StT0CUliCZQ++ZU7SMWSYzMrrqbGW0Gg0mDWNJsKuuCbpGDJcTdMpmfQtZktfCWqbxPgSFWHF32lK14Iz2YOcNYQG2KQN8uOFa55D2afvyWoKS9BjaiaATd8zlrSyK/5GbWgJNBrMjAmBxyy/RsekjScTZla2oaVM6koQtq8Q1ckx40d5ALPqJo0GM6IQWAquhNHewHHEYOompW5lKKS0BMvOa0p0nDHL/kpdGKCFouk3MfTYBy7RtojxpG4SzkWkbxyY0hIsmlCLY8abYi+svYOmQC2YZg2hxXavhU0PJB1F9kbTdMpxOp+0pLIES7HHtx2cdAzZS/bxP1EfGO0bTClrYEJosPf/Iukospd80wzKKVwZCiktwaJzmIY2HZ823gx1Yx78Fa3p/LeWec2Bway+VQdlj0N+8qEUXTqfnKayBAHK5SK0zE06huwls/Z2gq6VTAjT+Q8uq3LWUms95tHLk44ie81gmg+gmLKb6T4ptSU45AP8pAVJx5B9YO/5KQ1B5YFT0mFi4DBLfgPRYNJRZG9NmEXsIlK4OwJIcQkWfWUIL+NQsRtz/yVMClP6ry5j6kNLWOzCrLkt6SiyD/ykhQyldCoU0lyCscNMmAkpu/dVVpj1d2E7lmtadJzLWUNzaLB3/whSucA+/fzUwxlKb1Wk9yvzQByXYcKspKPIPrL3XkRD4MlrWnRcMsCknME88Evo3ZB0HNknBtMyh2KczuuBkOISBBhyBt86P+kYsq9Kvdj7LqY1V3lAlfFlYs5gN96PWXt70lFkXzXNIHYutdcDIeUlWMTip+i64Li24R7s1keYmFMNjicNgaWm3Iu9/+Kko8j+mLSAYpobkLSXYOww2iYx7tnFP6ZmqF3XB8eJQmCZEDrsbeeCS9/957LETTmcIdK9riLVJRg/eRdkHaY9vrky9tbv0ECJ+iDVP7LjXmgMk3IGe8d5MLA16Tiyn8zEeRTj9B2a/XSpf0Qpxg60X3D8K/Vib/kWzaGnoCKsShZoyxvMQ5dC+7Kk48j+apyGwxN7TYeOawM+wM08IekYMhL6NmHvOI9JOaMVo1XGUCnAYM2tmNW3JB1HRsKkBQzF6S5AyEAJDsUxpuVACGuSjiIjoX0p9u7zacvroO1qYYEpeUO4/m7MQ5clHUdGiJtxPIMpvx4IGShBDxTLJZhyeNJRZKRsXoK596dMzltC3Y0+UYExTClYwlU3Yh/4BdoQnxK5WkzLgQxF6d0f+KTUlyDAACFu5vFJx5ARZDbci3ngEibnrUaECQmNYUreEiy9CvPI75OOIyNpyhEUy6VMPKXJRAkOxjGm7WCwukdPmpi1d2Dvv5jJeUttkP5pm2qSs5URoF1yGWbZ1UnHkRHmZj2XfrLxeJmJEnQeynEZ2hYlHUVGmFl/F/aWbzLRlmnSPsIxkbeWyXmLufdnmNU3Jx1HRlpQwEyan4mpUMhICQL0+xA347ikY8ho6FqNvf7LNBY7dMTaKCsElra8wd71f5gNi5OOI6NhyqGUyiXSvTvwKZkpwcHYwbSj0ENkSg11Y2/8OjXbHmVK3hBowcyIawwtk0Kwt58LWx5OOo6MkspUaC7pGGMmMyUYe4/zHibqGLXUcmXs3ecTLL+aqQWrvYQjJDCGKXlD0+Bm7PVfhvblSUeS0WJDTNvBDMZR0knGTKYeJfp9gJ9xbNIxZJTZpX/GLr6AthzU6XSZ/VIXWKYWLLnlf8Xe+HUYaE86koymyYdQjsupvmvEP8rUI8Rg7FSCWbHpQexN/0lL3MvkHNpPuJcslXsBtrhe7E3/jVn6F7QHMP3czBPp99lYFfqkTJVg2Xl8UIAJs5OOImOhdyP2718gv+zPTClYJoRGV4SHoSawTKsJqFl3O/bvX4SedUlHkrFgAszUwyvrJzIkUyUI0BeDm/OCpGPIWPExZvk12Gs/T0PnUqYVLDXaU7hTBmgJDa22jL3j+5iHfg0uO9eGMq9tIVEcpf7A7H+UuRLsjz1m5nEQ5JOOImNpqAt753kEd/2QVt9PWw6tIN3OUFn5Ob0moG7rQ9hrPwfbHk86lowxN+eF9GVsKhTArOntz1btA205T83Dl8Ga25OOIkmwIW7+S2D+mfRGnp4oW9M/TzJAQ2grhwxsfhj76B+gb3PSsSQJhUbci/+TDYNR5q78Zq/2gV4Xkp93BlYlmE0uwj5+Jay9ncYj30xj63z6Ik9fnP57p8HTy8/Cloexj/wB+jYlHUsS5A94HoPlOHMFCBktwaE4hvo2aJwGvRuTjiNJGWjH3v5daJhCw7wzaJx1AkNRTK+zFF36RofPGPlteUTlJ9sZ/NwX0esMWVwBnMnpUIAJoaVxwx2YB3+ZdBSpFmEBP+sk/PwzcWENPS5gIBr/z45z1lBvoT60Kj95tsmHUj7mPWwqJx0kGZktwcAYpuYN9i8f0wo4eba2gyvXDSfOoz+K6YshGkdTpYEx1AWWBhtjXYRZfStmza3QvyXpaFJl3Ekfoaup8nOeRZmcDoXKMWpl5yhMPxrW3ZV0nKryoxvW8LObn9obNlCKedfzZ3Hqwon88Lo1lMqOtz1vBs9f1LrLz+Gc57gv3UpNrrIAeebEGi79l6O56OZ1XHzben767iOY3VrLnx/YwkuPnDzqX9Ne2/ooduujUNdKw9wXUT/rRLwNGIxh0BuKsau6EWLOGmoDSx0xoTX4DYuxq2+BjhVJR5NqVdMMEw9iYCi7A4HMjgQBaoOAiUObsDd+LekoVe2NP7yPL549nw9f8ghffvV8DIaPXPIIf/3EcUyo2/lBuyu2DPD1K5bz43cf8YzXv/X8+3nLSTPY1F3kxHnNdA6UOXFey1h8GfuvaQZ+yuH4GcdiGqdTKg8xRI6i95TGuBQNlVsa5QJL3sfUBAYTDWE23IPZcG+l+Hz6rmvKyHKLzmbgwBfSGWW2BrI7EoTKzXZpnAb1kzVNtAu3L+9kYn2OXGj58IsP3FFY05oLbOou7rIEH93Qx6LpDTt9W1NtyPLNA9z5RBdvO3nmqGUfcT3rMT3rMcv+CrlaCq3zybcuxE8+BNMwhSgqUvaGsgmJfWWlaeQqv+7PQ0zOGnLWksNTICIMQqwN8H1bYNsabOcq2PqorvPJ3jEW5jyfvmzOgu6Q6RIE6I8cDXNegFlyWdJRqtLPblnPO0+dybzJdcybXEfsPNc+vI1y7DloSv0uP+6RDb385YGt/PGeTbTU5/iP1yzgOQdMwBhDZ38Za6GhMI5//MqDsOlBzKYHMQ8DNkeu5UByDVPwda34+slQNwlqWzCFBrz3uLhM7BwRFg8YPBaP4akbfBnAGLPjxQZ5/FA3dK3DdKzE9KyrHGM20IGpuglZGVemHE6EoZyl07J3Yhw/Co2M3thTf8DzMI9dAdFQ0nGqyqauIus6Bjl+bvOO1/38lnV85+pVfOplcwnsrk9cmdSQ5z9es4DnLZjIXx/cyud/t5QrP3YcLz2ije/9bTUvPbKNLT1FXnXuPVz8viNpqBnnP4quDO3LoH3ZM0rtSSZXh62dSFjbQqG2BUxQ+Zi4/NSvT/+9K0McwVAnRgu3ZBS4g15Mrw+BbA8FM31N8EmtIdSuuBqz7C9JR6kqF9ywhmLk+NDpBz7j9Zu6irzuvHv5xfuO4oBJtcP6XM/9ym1c+bFjaW2oHFd38a3r+dvD2zh14UTmttXywkMmjXR8EdmVhim4F3yeDUPZOyHmH2Xu7NCd6XEGP/9MnSf6D65Zso3TD62U0+ptgzy2sQ+Aqc0FDp/ZyMqtA7v82MvufOoQgmLZ0T1Yxmw/q/OOFZ0cP3cC3nua60J6hrL9TFRkrLlFZ9MbVd8K5ySoBKncYqnowR94StJRqkZnf5l1HUMsmlZZ3LKlp8hHL3mUnsGILT1FHlrXy8Ezdr7wBeDPD27hivs201+M+f61qzhiVhMT6yuLaB7f2M/CaQ0YY+gbimko6K4OImOmvg2mHkFvRs/M/Ucqwe26Y4tf8LLKtRph8cpujpjVuOP/j5vbzCuPmcJLv3U3b/6/+/n3VxzElKYCG7uGeMV3Fj/r47/wyvn85KZ1nPq121m6qZ9vv/FgALoHysxoqQHglAUtXHrnRo6dM2FsvigRwS06iz6NAnfQNcGnmZyD/CO/xay+JekoIiIjr64V98Iv61rg02gk+DRdscUvOquyf0ZEJGXcwrPoS8F5uCNJj/ZPU3KOyOZhxrFJRxERGVm1E2HGMfRm+HSYnVEJ/oNuZ3EHv5Jn7/QSERm/3MKX0xc5tBzmmVSC/2AodsS5Oph2ZNJRRERGRk0zzDye3lgV+I9UgjvRHQe4Q16ddAwRkRHhF7yU/igm4yek7ZRKcCcG4xhXmADTjko6iojI/ik04WefRE+sBtwZleAudMQGd8SbwI7zMy1FJNP8wpdrFLgbKsFdKMaOks3j552edBQRkX3TMBU/+7kaBe6GSnA3OiODX/gyKDTu+Z1FRKqMO+qtdEdeo8DdUAnuRuQ9/ZHDHfrapKOIiOydqUfimmbSpzNCd0sluAfdkYfpR8OEcXQHdBHJNhvijnwLHbH2O++JSnAPPNAVedxRb0s6iojIsPj5Z1K0OYraF7hHKsFh6I8crn4KTHtO0lFERHavphk//yV0RhoFDodKcJg6YoM7UlsmRKS6uSPeQF/kiL1WwwyHSnCYirGjaHL4g85IOoqIyM5NPAjfdig9OiR72FSCe6ErMvgFL4WCbgIrItXG4J7zNrpidKukvaAS3AuR95VT2I96a9JRRESewR9wClGhiYEoTjrKuKIS3Es9kcdNWgDTj0k6iohIRaEJf9hr6NBimL2mEtxLHmgvg3vO23SSjIhUBXfMO+mLoayjYfaaSnAflJyjLwZ39DuTjiIiGednn0zcPKdysIfsNZXgPuqOPK5lLn7mCUlHEZGsqmvFH/562qOkg4xfKsH9sC0Cf+SboUarRUVkrBncse+lR9Og+0UluB/Kzm+fFn1X0lFEJGP8vNOJ6qfSqwOy94tKcD91R464+QD87JOSjiIiWdEwFX/wWWzTNOh+UwmOgPYI/OFvgNqWpKOISNqZAHf8++mKvI5GGwEqwRFQdp7eGNwx7046ioiknFv4ckqFZvo1DToiVIIjpCdyxE0z8XNfmHQUEUmr5gPgoNO1KX4EqQRH0LYy+ENeBS1zk44iImkT5HHHvZ/OsqZBR5JKcARF3ldOkznxQzpNRkRGlDv6HQyFtQzoRrkjSiU4wobimD4f4E74EBh9e0Vk//k5pxG3HUJHOekk6aNH6VHQHXlK9VPxh70u6SgiMt61zMEf+urK5Zaks6SQSnCUtJc9bvbJeN1tQkT2Vb4Rd+KHaC9XLrfIyFMJjhIHbC17/NFvh8ZpSccRkfHGWNwJ/0KfDxmKdY/A0aISHEVl5+kse9xzPwxhTdJxRGQc8Ue8kVLDNN0dYpSpBEfZQOwYtLW4Y9+bdBQRGSf8Ac8jnnkC28oqwNGmEhwDHZEnmjgPv+Cfko4iItVu4jz84a9na8lrIcwYUAmOkW1l8AteClMOTzqKiFSr2pbtC2G8FsKMEZXgGIm9Z2vJ4457b+XoIxGRpwsLuJM+So8LGNKG+DGjEhxDJecqJ8qc9BGoa0s6johUCxviTvoog7kJuj/gGFMJjrGhOKYrDnCnfALyDUnHEZGkGYs74YMU66fRoZWgY04lmID+2NFnanAnfwyCfNJxRCRB7uh3UG6ep5WgCVEJJqQ78gwVWnEn/iuYIOk4IpIAf9jriaYcwVYVYGJUgglqjzzFptmVxTLo/mAiWeIWvJT4gJO1FSJhKsGEbSt7yq2LcM95a9JRRGSM+ANPxc//J7aUPFoGkyyVYBXYWvbE047BH/LqpKOIyCjz04/BH/Y6tpScbo5bBVSCVcADW0qeeM4LdKqMSJq1HYw/+u1sKTlthq8SKsEq4dhehPP/CbforKTjiMhIa5mLO/4DbC15yk4FWC1UglUk9p7NJUc890X4w16fdBwRGSmTFuFO+gjtUeXQDKkeKsG9tPqJFTyxbNmofX7nKyPC8uyTcM95G1o1KjLOTTsKd+K/sK2MjkOrQpkuwVKxyOaNG7np79dy09+vJY5j3B6epf3iwgtZ9uijo5rryanRaNoxuGPfDSbTf00i45afdSLumHexpeQoagRYlcya3v7MTk6/8aUv4aBFi5g5+wAWHXoYfb29/PHSX7Nty2Zmz5nLiqWP89Pf/YELv/ddOrZtw3vPiqWPs+DgQzC2UkzeOfr7+/n+z34+4vkM0JYz5DqXYe/8IXjdXVpkvPBzX4Q7+JVaBFPlMl2Cbzv7LH5++RVc+rOLmDZzJqe+6HSuufJPrH7iCd7zbx/m3ee8lgsu/Q3GVKYkf/uLi6lvbOSfzn7lmOaclDMUelZjb/8euPKY/tkisvfcwlfgDjqjsthNBVjVwqQDJMU5x0B/H//79a8RRxHr1qwhsAG/u+QXfPLLX+E7X/sq+Xx+RwGuWPo4D913H6e/7GV86G1vJQgr37pyqcghRxzJhz75qVHLuq3saW06gJrnfRx723cgKo7anyUi+8cf/gbiWSexpeTQItDql9mRYLlc5v++/S1e9upXM3f+AgAuv+wyTjzlFKZMm8bVV1zBkccey9Tp0ymXy3zqXz7AG97+Do4/6eQdn+OOm2/mJ+d9j499/ossOvTQUc88MTTUFtuxt3wTygOj/ueJyN4wuKPfQTT1yMq9Q5OOI8OS2ZFgLpfjqOOO4zP/+iGmTp8BwMrly7n2z1dibUB3Vyfr167hje94J1/798/Q39tLPl/Y8fGbN27ggu/+L+f+5CIaGhvHJHNH5GkutFL//H+vjAgH2sfkzxWRPTAB7vj3U564QGeBjjOZLUGAMAxZcMghvOyVlePKLvz+93j1m95MbU0tD9yzmCAM6enu5nkvfCEb1q2jp6uT973x9dTU1hJHMe1bt/LZD/8b5XKJV73hjZzxspePeuauyBOFTUx4weexd34f2peP+p8pIruRq8ed+CGKDdN1O6RxKNMlaIaxB2/KtGm85Kyz+ckPzqOpuYXzf3UpAB3btvGNL32R//r+eaMd81n6IkcUWFqf+2HMQ5dhVt885hlEBGiagTvpI/STp0sFOC5lugQ9nqWPPEJPVzcAWzZt4ve/vGTHdOhpZ75klx8bxzEmwX3sQ7FjszO0HfY6gubZmAd/BV5XIUTGip9+DP7ot9NZ9gxoE/y4lekSLJdKPP/0M/jgJ/7fs9527Z+vYtWKFU+9b7FIuVQCYMPatXzqXz7Iq9/4xjHLujOR92wqeiZNP5580wzsHd/XghmRUWfwh7wKN+c0tpaczgEd5zK7OjRtmkNDvR/C3vod6NuUdByRdAprcMe/n6h5DlvLXlsgUkAlmCL1gaU5BHv3j2DLkqTjiKRL/WTcyR9lwNbRGelhMy1UgimTt5a2vME8fiVm2dVJxxFJh8mH4Y57L10x9Ee6/pcmKsEUCoyhLQdB+1LsvT/VdUKR/eAWvBQW/BNbS163QUohlWBKGWBCaKg3Efbu82Hb0qQjiYwvhUbcMe8mbj6QrWV0BmhKqQRTriawtOYMZuWNmEf+oDtRiAzH1CNxR7+DPmfo1vW/VFMJZoA10Boa8qUu7J0/gL7NSUcSqU5hAXfkm/FTj2JbWXeBzwKVYIbUB5bmnMEs+S1m1Y1JxxGpLhPn4o57P4OmQGek8z+zQiWYMaExTMpB0LUSu/hCKPUlHUkkWSbAH3w2fu5pdJRhMNYlgyxRCWZUc2iotw67+ALY8kjScUSS0TAVd8IHKOWbaY+0+T2LVIIZVggsraHBrL8L+/BvoTyYdCSRMWLwc1+IP+SVdEVee/8yTCWYcZbKqLDWOsyDl2LW3ZF0JJHR1TgN95y3EzVMpb1cOYNXskslKEDlpJmJoSfo31zZYN+7MelIIiMrrMUf8kr87JPojqAv0rU/UQnKP2gIAyaEYFbdjHn0coiLSUcS2W9+1nPxh5/DoLN0RR5NfsqTVILyLNZASwg1xNgHLoEN9yYdSWTfTJiJe87bieva6IiM9v3Js6gEZZcK26dIbc9a7H0/h/4tSUcSGZ5cHe7Q18DM47XwRXZLJSh71BhamkKDWXEdZulVEJeSjiSyCwZ/wMn4w17LQFw58kz1J7ujEpRhCYyhJfQUjK/cpmnljeCipGOJPKV1Pu7INxPVNNMRGd3xXYZFJSh7JWcNzYEnj8M8ejlm9S06lFuS1XoQ7tDX4Run0RUZBnTii+wFlaDsk7y1TAgceR9hHvkDZu3t4DXxJGNo4kG4w16Lb5xOd2ToV/nJPlAJyn7JW0tz4Mi5Evbh38G6u0BHD8tomjgPd+hr8U0zVH6y31SCMiIKgaXZOsJ4ELvkt9u3VehHS0bQ08svNvRrs7uMAJWgjKiawNIceIJSL/bRP8KG+3TNUPbPxLnby2+myk9GnEpQRkVNEDDBxoTGY564rrKatNiTdCwZL0wA047CzT8T3zBV5SejRiUooypnDY0WanMBbFqCXX41dK5MOpZUq5pm/JwX4Oc8n8gbenzAoMpPRpFKUMaEAerDgKbAYwY7scv+Cuvv1l5DqZi0CDf/xdC6gIEops+hfX4yJlSCMuZqAkuTdeSsway8CbPyOhjsTDqWjLVcLX7WSfiDzsCFtfQ4y0AUazmVjCmVoCQmNIaGoDJCpH0pdtVNsHmJRodpN2EWbt4ZmOlHMxhF9Dqrg60lMSpBSZwB6sKABhORC0L85iXYNbfBlke0sjQt6trwM4+vnOuZb6A3hv7YoRlPSZpKUKqKNVAXBNSbiNAGsOl+7JrbYdvjOpFmvKlpxs84Fn/AKVDXykAU0+90OyOpLipBqVqBMdQFlnoTEVgL6xdj194B7cvRRvwqVd+Gn340fuaJ0NDGYDmi31uKsYpPqpNKUMaF0BhqA0uDjbF4zLq7MZsegPZlurVT0ppm4mccg595PBSaGIwc/d6o+GRcUAnKuBNuHyHWmohcmMd3r8VsuBez5WHoWZ90vPRrmAqTFuKmHg6t8/HAQAwDmuqUcUglKOOaoXI6TY1x1AZgvK+sNN30UOU6Yv+WpCOOf/WTYdIC3NQjKqVnAoqxY5DKr7HXQ4iMXypBSZXAGGoCSw0xhcBifAzty7Cbl0DX6spIUVswdq+utbJ5fcrh0LYAbI6hKGKIkCGVnqSMSlBS7emlmDcQ5Ar4wS7oXoPpeALTvQa610GpN+moYy/IQ+N0mDAT3zwb3zwH0zAFb8yO0ivGjkilJymmEpTMyVlDzlryxlPwEWGuUFlc07MB07FiezGuhf6tKdmWYaC+rVJ2TTPxLXOhaTqm0EBULlL2hpIJKTlH2WnvnmSLSlCEyoixUoxQoEwuCLBBAR8NwlA3DLRj+rdgBrZVjngb7ITBDhjqIdntGgbyDVDbDDWVF1/bjK9rq0xr1jZjalpwrkw5jimaHGUPZY3wRACVoMhuBcYQGENoK78GPiZHXPl9EGJsiC8PVIpysBPKgxANYaJBTDRUGWHGJYhKT/0+Lj71/wBBrvJid/driA8K+FxdpdxqmqGmCZOrw7sYF5eJvSfCEJkcsYfYV67fRc5rV6XILqgERfZTYAyhMVhrsIAxBkNl5ar1DoOr7G3EV97+5PsYA4D3vvICT3sx23+1ePPkZ6i8Lfae2PnKrxrNieyXMOkAIuPdjjLa7eXDJ2txb9/2ZPWl4dqkSPWxSQcQERFJikpQREQySyUoIiKZpRIUEZHMUgmKiEhmqQRFRCSzVIIiIpJZKkEREckslaCIiGSWSlBERDJLJSgiIpmlEhQRkcxSCYqISGapBEVEJLNUgiIiklkqQRERySyVoIiIZJZKUEREMkslKCIimaUSFBGRzFIJiohIZqkERUQks1SCIiKSWSpBERHJLJWgiIhklkpQREQySyUoIiKZpRIUEZHMUgmKiEhmqQRFRCSzVIIiIpJZKkEREckslaCIiGSWSlBERDJLJSgiIpmlEhQRkcxSCYqISGapBEVEJLNUgiIiklkqQRERySyVoIiIZJZKUEREMkslKCIimaUSFBGRzFIJiohIZqkERUQks1SCIiKSWSpBERHJLJWgiIhk1v8H3ZZZz2vrJBUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_churn = data['Churn'].value_counts()\n",
    "x = data_churn.index\n",
    "y = data_churn.values\n",
    "\n",
    "fig = plt.figure(figsize=(8,8),facecolor='#e2f3f5')\n",
    "ax = fig.add_subplot()\n",
    "ax.pie(y, startangle=90,labels=['留存','流失'],colors=['#ffaf65','#f36886'],autopct='%3.1f%%', counterclock=True, wedgeprops={'width': 0.6})\n",
    "plt.title('用户流失比例')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1dc1c568",
   "metadata": {},
   "source": [
    "**(4) 用柱状图可视化性别、老人、配偶以及经济是否独立对流失率的影响，并给出结论**\n",
    "\n",
    "思路：前面我们对全部用户进行了分析，这一题可以将数据分开，分别按照性别、老人、配偶等字段将数据进行拆分，再分别对这些数据进行是否流失的可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "eb3ec56f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = ['Gender', 'SeniorCitizen', 'Partner', 'Dependents']\n",
    "# 创建四个画布\n",
    "fig,ax = plt.subplots(1,4,figsize = (24,4))\n",
    "# 分别对需要绘图的字段进行循环，依次绘图，也可以每个图单独处理\n",
    "for i in range(0,len(columns)):\n",
    "    # 对于每一个子图需要绘制一个x轴堆叠的柱状图，实质是在一个子图中绘制多个柱状图\n",
    "    widths = 0.35\n",
    "    # 获取图例的标题\n",
    "    labels = data['Churn'].unique()\n",
    "    # 获取x轴的标题\n",
    "    xlabels = data[columns[i]].unique()\n",
    "\n",
    "    # 构造x轴数据\n",
    "    x = np.arange(len(xlabels))\n",
    "    # 将整个数据根据labels进行分割，形成多个数据\n",
    "    ys = [data[data['Churn'] == label] for label in labels]\n",
    "    # 针对每一个数据应该是图例中的一个维度，每一个数据都可以绘制一个柱状图，这个柱状图的x轴是指定的字段，y轴是人数\n",
    "    for j, y in enumerate(ys): \n",
    "        offsets = j * widths\n",
    "        _y = y[columns[i]].value_counts().values\n",
    "        ax[i].bar(x + offsets, _y, width=widths, label=labels[j])\n",
    "    ax[i].set_xlabel(columns[i])\n",
    "    ax[i].set_ylabel(\"count\")\n",
    "    ax[i].set_xticks(x+widths/2)\n",
    "    ax[i].set_xticklabels(xlabels)\n",
    "    ax[i].legend(['留存','流失'])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6af2eb4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = ['Gender','SeniorCitizen','Partner','Dependents']\n",
    "fig,ax = plt.subplots(1,4,figsize = (24,4))\n",
    "for i in range(0,len(columns)):\n",
    "    sns.countplot(x=columns[i],hue='Churn',data = data,ax=ax[i])\n",
    "    ax[i].legend(['留存','流失'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "200e48be",
   "metadata": {},
   "source": [
    "结论\n",
    "1. 性别对流失率几乎无影响\n",
    "\n",
    "2. 老人的流失率占比比年轻人流失率高\n",
    "\n",
    "3. 没有配偶的用户流失率高于有配偶的用户\n",
    "\n",
    "4. 经济未独立的用户的流失率远高于经济独立的用户的流失率"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9e1fc633",
   "metadata": {},
   "source": [
    "**(5) 用柱状图可视化多线业务、网络安全服务、在线备份业务、设备保护业务、技术支持服务、网络电视和网络电影对客户流失率的影响，并给出结论**\n",
    "\n",
    "思路：同上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c54660a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第一行\n",
    "columns = ['MultipleLines','OnlineSecurity','OnlineBackup','DeviceProtection']\n",
    "fig,ax = plt.subplots(1,4,figsize = (16,4))\n",
    "for i in range(0,len(columns)):\n",
    "    width = 0.35\n",
    "    labels = data['Churn'].unique()\n",
    "    xlabels = np.sort(data[columns[i]].unique())\n",
    "    x = np.arange(len(xlabels))\n",
    "    ys = [data[data['Churn'] == label] for label in labels]\n",
    "    for j, y in enumerate(ys):\n",
    "        offsets = j * width\n",
    "        _x = x + offsets\n",
    "        _y = y[columns[i]].value_counts().values \n",
    "        ax[i].bar(_x, _y, width=width, label=labels[j])\n",
    "    ax[i].set_xlabel(columns[i])\n",
    "    ax[i].set_ylabel(\"count\")\n",
    "    ax[i].set_xticks(x+width / 2)\n",
    "    ax[i].set_xticklabels(xlabels)\n",
    "    ax[i].legend(['留存','流失'])\n",
    "\n",
    "# 第二行\n",
    "columns = ['TechSupport','StreamingTV','StreamingMovies']\n",
    "fig,ax = plt.subplots(1,3,figsize = (16,4))\n",
    "for i in range(0,len(columns)):\n",
    "    width = 0.35\n",
    "    labels = data['Churn'].unique()\n",
    "    xlabels = np.sort(data[columns[i]].unique())\n",
    "    x = np.arange(len(xlabels))\n",
    "    ys = [data[data['Churn'] == label] for label in labels]\n",
    "    for j, y in enumerate(ys):\n",
    "        offsets = j * width\n",
    "        _x = x + offsets\n",
    "        _y = y[columns[i]].value_counts().values \n",
    "        ax[i].bar(_x, _y, width=width, label=labels[j])\n",
    "    ax[i].set_xlabel(columns[i])\n",
    "    ax[i].set_ylabel(\"count\")\n",
    "    ax[i].set_xticks(x+width / 2)\n",
    "    ax[i].set_xticklabels(xlabels)\n",
    "    ax[i].legend(['留存','流失'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d6f11306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = ['MultipleLines','OnlineSecurity','OnlineBackup','DeviceProtection']\n",
    "fig,ax = plt.subplots(1,4,figsize = (16,4))\n",
    "for i in range(0,len(columns)):\n",
    "    sns.countplot(x=columns[i],hue='Churn',data = data,ax=ax[i])\n",
    "    ax[i].legend(['留存','流失'])\n",
    "    \n",
    "columns = ['TechSupport','StreamingTV','StreamingMovies']\n",
    "fig,ax = plt.subplots(1,3,figsize = (16,4))\n",
    "for i in range(0,len(columns)):\n",
    "    sns.countplot(x=columns[i],hue='Churn',data = data,ax=ax[i])\n",
    "    ax[i].legend(['留存','流失'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "965fdbb6",
   "metadata": {},
   "source": [
    "总结\n",
    "\n",
    "1. 除多线业务 MultipleLines外，其余6个变量在无网络服务的情况下，流失率都很低且低于有网络服务时的流失率，说明没有网络服务，不是导致用户流失的原因；\n",
    "2. 在多线业务的问题上，未开通多线业务的流失率略低于开通多线业务的流失率；\n",
    "3. 网络安全服务、在线备份业务、设备保护业务、技术支持服务、网络电视和网络电影等六项业务在开通的情况下的流失率比不开通的时候的流失率低。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3a76ff5e",
   "metadata": {},
   "source": [
    "****(6) 用柱状图可视化付款方式对客户流失率的影响****\n",
    "\n",
    "思路：同上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "461501d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2494a7d7070>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置画布大小\n",
    "plt.figure(figsize=(10,5))\n",
    "width = 0.35\n",
    "labels = data['Churn'].unique()\n",
    "xlabels = data['PaymentMethod'].unique()\n",
    "x = np.arange(len(xlabels))\n",
    "ys = [data[data['Churn'] == label] for label in labels]\n",
    "for j, y in enumerate(ys):\n",
    "    offsets = j * width\n",
    "    _x = x + offsets\n",
    "    _y = y['PaymentMethod'].value_counts().values\n",
    "    plt.bar(_x, _y, width=width, label=labels[j])\n",
    "plt.xlabel('PaymentMethod')\n",
    "plt.ylabel(\"count\")\n",
    "plt.xticks(x+width / 2, xlabels)\n",
    "plt.legend(['留存','流失'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
